skimage 0.17.2

ParametersReturnsBackRef
hough_circle(image, radius, normalize=True, full_output=False)

Parameters

image : (M, N) ndarray

Input image with nonzero values representing edges.

radius : scalar or sequence of scalars

Radii at which to compute the Hough transform. Floats are converted to integers.

normalize : boolean, optional (default True)

Normalize the accumulator with the number of pixels used to draw the radius.

full_output : boolean, optional (default False)

Extend the output size by twice the largest radius in order to detect centers outside the input picture.

Returns

H : 3D ndarray (radius index, (M + 2R, N + 2R) ndarray)

Hough transform accumulator for each radius. R designates the larger radius if full_output is True. Otherwise, R = 0.

Perform a circular Hough transform.

Examples

This example is valid syntax, but we were not able to check execution
>>> from skimage.transform import hough_circle
... from skimage.draw import circle_perimeter
... img = np.zeros((100, 100), dtype=np.bool_)
... rr, cc = circle_perimeter(25, 35, 23)
... img[rr, cc] = 1
... try_radii = np.arange(5, 50)
... res = hough_circle(img, try_radii)
... ridx, r, c = np.unravel_index(np.argmax(res), res.shape)
... r, c, try_radii[ridx] (25, 35, 23)
See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

skimage.transform.hough_transform.hough_circle skimage.transform.hough_transform.hough_circle_peaks

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